CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs

Naren Akash R. J, Arihanth Tadanki, Jayanthi Sivaswamy

Abstract: We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract informative features from specific anatomical regions. Furthermore, it captures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accurate, effective and efficient retrieval, particularly for less prevalent findings. CheXtriv outperforms state-of-the-art global and local approaches by \(18\%\) to \(26\%\) in retrieval accuracy and \(11\%\) to \(23\%\) in ranking quality. The code is available at https://github.com/cvit-mip/chextriev.